Advanced Methods for the Optical Quality Assurance of Silicon Sensors
This work addresses quality assurance in silicon sensor manufacturing, which is incremental as it builds on existing optical inspection methods with improved algorithms.
The researchers tackled the problem of detecting defects in silicon microstrip sensors by developing pattern recognition algorithms to analyze microscopic scans, achieving a recognition and classification rate of >90% for defects like scratches and shorts, and demonstrated that neural network techniques could further improve this rate.
We describe a setup for optical quality assurance of silicon microstrip sensors. Pattern recognition algorithms were developed to analyze microscopic scans of the sensors for defects. It is shown that the software has a recognition and classification rate of $>$~90\% for defects like scratches, shorts, broken metal lines etc. We have demonstrated that advanced image processing based on neural network techniques is able to further improve the recognition and defect classification rate.